Electricity Energy Forecasting for Turkey: A Review of the Years 2003–2020

Özkurt, Nalan; Oztura, Hacer Sekerci; Güzeliş, Cüneyt

doi:10.5152/tepes.2021.21019

Cited by 2 publications

(5 citation statements)

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“…In this manner, as a disadvantage of the method, time series analysis may suffer from the weak data, including statistical problems with generalization from a unique work, difficulty in receiving the appropriate techniques, and issues with the accurately definition of the correct model to represent the data. Therefore, we have collected the real data from different sources and confirmed them by checking different references [1][2][3]16,20,25,27,28].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The first forecasting studies in Türkiye were performed by the Ministry of Energy and Natural Resources (MENR) during the 1970s [16]. Other important development on the subject was the estimation and planning for the energy need which was made by the MENR and the State Planning Organization (SPO) in 1984 [16]. The historical development of the energy market in Türkiye can be briefly summarized as follows: In 2001, energy market regulatory authority (EMRA) was founded.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Turkey

Kurt

Kasap

Çelik

2022

Journal of Energy Systems

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In the present paper, a forecasting study on the monthly electricity generation of Türkiye from the conventional and renewable resources is performed. The effect of the CoVid-19 pandemic on the sector has been considered. For this aim, the trend before the pandemic has been initially considered and later the post-pandemic situation has been handled. It has been observed that the electricity generation supply/demand mechanism changes drastically compared to the pre- and post-pandemic cases. The rate of the generation from the renewable resources especially shows a sharp variation compared to the rates from the fossil fuels. According to the forecasting scenario, in 2021, the electricity generation shows different attitudes with regard to the resources used. In 2022, especially increasing trends are expected for wind, biogas, natural gas, imported coal and fuel oil, whereas diesel and mineral coal are expected to be decreased in Türkiye.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Turkey

Kurt

Kasap

Çelik

2022

Journal of Energy Systems

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“…The energy deficit estimation of Türkiye for the period of 2012-2020 is modelled employing artificial neural networks in another work where it is estimated that domestic electricity production would increase in the mentioned period (Esen, 2013). The modelling methods for the electricity supply and demand of Türkiye are summarized in another study in which it is shown that feedforward neural networks, recursive models, swarm intelligence, fuzzy logic, autoregressive models, regression models and Grey prediction models are used for the modelling and forecasting the electricity supply and demand of Türkiye (Ozkurt et al, 2021). In another work, the recursive multilayer perceptron model is utilized to model the hourly electricity demand of Türkiye in which the average error of 10.65% is achieved (Topalli and Erkmen, 2003).…”

Section: Literature Analysismentioning

confidence: 99%

“…Similarly, modelling the hourly electricity demand of Türkiye for the 2012-2014 period is investigated in another study using multilayer perceptron, gradient-descent momentum, Levenberg-Marquardt algorithm and Broyden-Fletcher-Goldfarb-Shanno algorithm and it is concluded that the Broyden-Fletcher-Goldfarb-Shanno algorithm provides accurate results (Gokgoz and Filiz, 2020). The long-short term memory method is utilized in another work for the estimation of the hourly electricity demand of Türkiye for the 2016-2020 period and it is observed that mean absolute percentage error lower than 5% is reached (Ozkurt et al, 2020). In another study, artificial neural network with improved genetic algorithm is used for the modelling of the hourly electricity demand (Srinivasan, 1998).…”

Section: Literature Analysismentioning

confidence: 99%

Estimation of the Electricity Supply and Demand of Türkiye Employing Deep Learning Networks

Tuncsiper¹

2023

IJMRA

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Electrical energy, one of the indispensable elements of the economic and social life, is an energy source that has to be provided without interruption in order to ensure the continuity of development. In this work, the monthly electricity supply and demand of Türkiye for the period of 2016-2022 are modelled using advanced machine learning techniques. An autoregressive deep learning network consisting of four hidden layers is developed for the modelling of the electricity supply and demand da ta having the form of multilayer perceptron artificial neural network. The electricity supply and demand data of Türkiye is gathered from the official sources and then investigated utilizing the seasonality analysis. Then, the deep learning network is developed in Python programming language. The 70% of the available electricity supply and demand data are used as the training data whereas the remaining 30% of the data is the test data. The randomized selection of the train and test data are performed using the classes available in the SciKit-Learn library of the Python programming language. The performance of the developed deep learning network model is evaluated both by plotting the actual and the modelled data on the same axis pair and by using performance metrics of the coefficient of determination, mean absolute error, mean absolute percentage error and the root mean square error. The mean absolute percentage errors of the models have the values of 2.29% and 2.17% for the electricity supply and demand data, respectively, indicating the success of the developed deep learning model for the modelling and the estimation of the electricity supply and demand data. The developed model is considered to be useful for the economic and technical energy planners.

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Electricity Energy Forecasting for Turkey: A Review of the Years 2003–2020

Cited by 2 publications

References 0 publications

Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Turkey

Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Turkey

Estimation of the Electricity Supply and Demand of Türkiye Employing Deep Learning Networks

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